Repetitive Tasks Research Articles

De Quervain's tenosynovitis as an occupational disease in agricultural worker: A case report.

De Quervain's tenosynovitis is upper-limb disorder, more frequent in women. It is rarely recognized as an occupational disease. In this paper we reported work related de Quervain's tenosynovitis in cow milker. Ergonomic risk factors were assessed by utilizing assessment of repetitive tasks (ART) of the upper limbs method. Physical examination and imaging exams were used. We presented a 36-year-old female agricultural worker (workplace cow milker) exposed to work factors (forceful hand/wrist exertions, repetition, extreme postures) for 14 years. She was suffering the pain in the right hand, mainly near the thumb for few years. She was treated conservatively and surgically, however disability was permanent. Clinical examination showed slight edema of the right hand, increased palm's sweating. Patient had reduced range of movements and grip strength. Report shows that work-related factors were strongly associated with diagnosed de Quervain's tenosynovitis. Hand function plays an essential role in carrying out activities of daily living and this disorder pose a relevant burden of disease. Exposure to biomechanical factors should be an important initial target for the prevention of De Quervain's tenosynovitis in the working population.

Exploring vowel errors produced in nonword repetition in children with speech and language disorders.

Accurate nonword repetition (NWR) is contingent on many underlying skills, including encoding, memory and motor planning and programming. Though vowel errors are frequently associated with childhood apraxia of speech (CAS), several recent studies have found that children with developmental language disorder (DLD) produce high rates of vowel errors in NWR tasks. This retrospective analysis explored whether the overall frequency and types of vowel errors produced in NWR distinguish children with DLD, children with CAS, children with speech sound disorder (SSD) and children with typical development (TD). We present data for 24 children (six per DLD, CAS, SSD and TD groups), ranging in age from 50-92 months. Children with DLD, CAS and SSD showed similar articulation scores and children with DLD and children with CAS showed similar expressive language scores. Total vowel errors, total monophthong errors, monophthong substitutions, diphthongization errors, total diphthong errors, diphthong substitutions and diphthong reduction errors were calculated by syllable length and group. Repeated measures analyses of variance were used to examine group differences. Children with DLD and children with CAS produced a higher frequency of total vowel errors compared to children with TD. Children with DLD produced more total monophthong errors than children with TD. Children with DLD and children with CAS produced more total diphthong errors than children with TD. For children with DLD, these were characterised by diphthong substitutions. For children with CAS, these were characterised by diphthong substitutions and diphthong reduction errors. For all measures, error rates in children with SSD did not significantly differ from any of the other three groups. Preliminary evidence indicates that children with DLD and children with CAS both show high rates of vowel errors in NWR tasks and weaknesses in encoding and memory. For children with CAS, additional motor planning difficulties are associated with an increased likelihood to reduce diphthongs. Children with SSD show more mild processing difficulties than children with DLD and children with CAS, though they do not perform as well as TD peers. Future work should replicate and further specify the processing weaknesses that affect vowel accuracy in NWR tasks in a larger sample. What is already known on the subject Nonword repetition (NWR) tasks are often included in diagnostic batteries to identify children with developmental language disorder (DLD). Poor performance on these tasks have historically been attributed to phonological working memory deficits in children with DLD. However, repeating nonwords relies on a number of underlying processing skills and many of these skills are affected to varying degrees in children with speech and language disorders. An in-depth analysis of vowel errors has the potential to reveal the shared as well as specific underlying processing weaknesses in children with DLD, children with childhood apraxia of speech (CAS) and children with speech sound disorder (SSD). What this paper adds to existing knowledge We found that children with DLD and children with CAS show low vowel competence compared to children with typical development. A nuanced examination of vowel error types further revealed that children with DLD and children with CAS show weaknesses in encoding and memory. Motor planning and programming weaknesses were unique to CAS. Children with SSD show more mild processing deficits and their performance did not significantly differ from any of the other three groups. What are the clinical implications of this work? Examining the types of vowel errors produced by children with DLD, children with CAS and children with SSD in NWR allows us to further specify the underlying processing weaknesses that differentiate these three groups. This research informs theoretical accounts of language processing in children with different types of speech and language disorders and has the potential to improve the diagnostic utility of NWR tasks.

Communicating robots’ intent through visual cues enhances human anticipatory behavior in human–dual robot collaboration

The present study aims at exploring the effect of communicating robots’ intent through visual cues, to the human on a complex human-robot collaborative task. Specifically, it aims to investigate (i) whether the use of such “anticipatory cues” will have a positive effect on task efficiency, human safety and collaborating fluency, (ii) the degree of this effect with varying robots’ speed and (iii) whether a retention effect will be observed after the removal of the cues. For exploring these issues, a human - dual robot industrial assembly task was designed in a Virtual Reality simulation environment and testing was carried out by 64 volunteer participants. Results showed that communicating robots’ intent through visual cues enhanced human anticipatory behavior, resulting in a significant improvement in human safety, team efficiency and collaborative fluency, in conjunction with a favorable subjective tendency towards the robots. However, the positive effect of the anticipatory cues was not found to increase with higher robot speed. Finally, the findings suggest that prior exposure to the cues made participants more confident in coordinating with the robots, even when the cues were removed from them, thus retaining their prior efficiency but with a negative effect on safety. In summary, the study provides evidence that use of anticipatory visual cues accelerates the legibility of robot movement and fosters human confidence and familiarization. The use of anticipatory cues seems promising for high-pace, non-repetitive interactions with collaborative robots or as a training aid in more repetitive human-robot collaborative tasks.

AAT4IRS: automated acceptance testing for industrial robotic systems.

Industrial robotic systems (IRS) consist of industrial robots that automate industrial processes. They accurately perform repetitive tasks, replacing or assisting with dangerous jobs like assembly in the automotive and chemical industries. Failures in these systems can be catastrophic, so it is important to ensure their quality and safety before using them. One way to do this is by applying a software testing process to find faults before they become failures. However, software testing in industrial robotic systems has some challenges. These include differences in perspectives on software testing from people with diverse backgrounds, coordinating and collaborating with diverse teams, and performing software testing within the complex integration inherent in industrial environments. In traditional systems, a well-known development process uses simple, structured sentences in English to facilitate communication between project team members and business stakeholders. This process is called behavior-driven development (BDD), and one of its pillars is the use of templates to write user stories, scenarios, and automated acceptance tests. We propose a software testing (ST) approach called automated acceptance testing for industrial robotic systems (AAT4IRS) that uses natural language to write the features and scenarios to be tested. We evaluated our ST approach through a proof-of-concept, performing a pick-and-place process and applying mutation testing to measure its effectiveness. The results show that the test suites implemented using AAT4IRS were highly effective, with 79% of the generated mutants detected, thus instilling confidence in the robustness of our approach.

Performance fatigability in adults with spinal muscular atrophy treated long-term with nusinersen.

Persons with spinal muscular atrophy (pwSMA) report progressive muscle weakness but also reduced endurance when performing repetitive tasks in daily life, referred to as "performance fatigability" (PF). Data regarding the effects of the new disease-modifying drugs on PF are scarce. Thus, our main objective was to examine PF in adult ambulatory pwSMA treated long-term with nusinersen. Six-minute walk test (6MWT) data from 14 adult pwSMA treated with nusinersen for up to 70 months were retrospectively analyzed to determine PF. Performance fatigability was defined as the percentage change in the distance covered between the last and first minute of the 6MWT. In addition, relationships between PF and other clinical features were assessed. Performance fatigability was found in 12/14 pwSMA (85.7%) prior to treatment. The mean distance walked in the sixth minute (71.1 m) was shorter than the distance covered in the first minute (81.8 m), corresponding to a mean PF of 13.1% (95% confidence interval (CI): 6.5-19.6, p = .0007). During treatment with nusinersen, there was a mean reduction in PF of 5.6% (95% CI: -10.0 to -1.3, p = .0148). We found no relationship between PF and fatigue as measured by the Fatigue Severity Scale. This study demonstrates the presence of PF as an independent component of motor impairment and as a potential therapeutic target in our cohort of adult ambulatory pwSMA. Furthermore, the observations in our cohort suggest that nusinersen may have a beneficial effect on PF.

Dynamic modeling of wheeled biped robot and controller design for reducing chassis tilt angle

Abstract The wheeled-legged robot combines the advantages of wheeled and legged robots, making it easier to assist people in completing repetitive and time-consuming tasks in their daily lives. This paper presents a study on the kinematic and dynamic modeling, as well as the controller design, of a wheeled biped robot with a parallel five-bar linkage mechanism as its leg module. During the motion of the robot, the robot relies on the tilt angle of the inverted pendulum, and this angle often results in the tilting of the chassis of the robot, presenting challenges for the installation of upper-body payloads and sensor systems. The controller proposed in this paper, which is developed by decoupling the primary motions of the robot and designing a multi-objective, multilevel controller, addresses this issue. This controller employs the pendulum pitch angle of the equivalent inverted pendulum model as the control variable and compensates for the chassis tilt angle (CTA). This control method can effectively reduce the CTA of such robots and eliminate the need for additional counterweights. It also provides a more spacious structural design for accommodating upper-body devices. The effectiveness of this control framework is verified through variable height control, walking on flat ground, and carrying loads over rough terrain and slopes.

B-013 Improving the Workflow of Blood and Coagulation Tests in clinical laboratory through the Utilization of Laboratory Automation Track Systems

Abstract Background The Laboratory Automation Track System (TLA) is an automated system designed for executing highly repetitive tasks within a laboratory, commonly applied in biochemical and immunological testing systems. It aims to reduce human errors in specimen handling, enhance the overall process quality, and provide better Turnaround Time (TAT) from specimen collection to result reporting. For the first time in our laboratory, we have integrated the blood and coagulation systems with TLA. Consequently, we seek to evaluate the differences in the testing workflow and reporting efficiency of the blood and coagulation systems after being connected to TLA. Methods Abbott GLP systems Track was the TLA system for connecting with blood and coagulation systems. Two Sysmex XN-9100 fully automatic hematology systems and two Sysmex CS-5100 fully automatic coagulation analyzers were developed for blood and coagulation system, respectively. TUNYEN Laboratory Information System was established for Laboratory information system (LIS). Results Through the analysis of quality indicators and integration with intelligent information systems, improvements have been made, including the reduction of blood collection tube usage, efficient manpower utilization management, tracking of specimen progress and achievement of timely reporting rates.(1)Approximately 2,915 blood collection test tubes are saved every month, with a converted cost of 0.15 USD per tube, and a total saving of 437 USD. And reduce the carbon emissions of 32 kgCO2e, comply with the spirit of ESG sustainable development.(2)The TLA no longer requires staff to collect and deliver specimens as in the past,reducing staff and enhancing work efficiency.One of manpower was saved and new inspection items were developed.(3)Tracking of specimen progress:The laboratory has set up a specimen tracking system to monitor the specimen delivery location and provide timely response to the clinical status.The one-hour TAT for blood and coagulation increased from 82.0% and 76.9% before improvement to 91.8% and 93.3% after improvement respectively. Conclusions The implementation of Laboratory Automation Track Systems (TLA) leads to a reduction in medical errors and specimen sample volume, while also enhancing accuracy and precision, thereby improving the safety of laboratory staff. Although the introduction of TLA may require an initial investment, it brings the benefits of automation, offering ease and efficiency to laboratory processes.

Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning

Robotic manipulators are widely used in various industries for complex and repetitive tasks. However, they remain vulnerable to unexpected hardware failures. In this study, we address the challenge of enabling a robotic manipulator to complete tasks despite joint malfunctions. Specifically, we develop a reinforcement learning (RL) framework to adaptively compensate for a nonfunctional joint during task execution. Our experimental platform is the Franka robot with seven degrees of freedom (DOFs). We formulate the problem as a partially observable Markov decision process (POMDP), where the robot is trained under various joint failure conditions and tested in both seen and unseen scenarios. We consider scenarios where a joint is permanently broken and where it functions intermittently. Additionally, we demonstrate the effectiveness of our approach by comparing it with traditional inverse kinematics-based control methods. The results show that the RL algorithm enables the robot to successfully complete tasks even with joint failures, achieving a high success rate with an average rate of 93.6%. This showcases its robustness and adaptability. Our findings highlight the potential of RL to enhance the resilience and reliability of robotic systems, making them better suited for unpredictable environments.

Reduced resting-state periodic beta power in adults who stutter is related to sensorimotor control of speech execution

ObjectiveThe primary aim of the current study was to determine whether adults who stutter (AWS) present with anomalous periodic beta (β) rhythms when compared to typically fluent adults in the eyes-open resting state. A second aim was to determine whether lower β power in the RS is related to a measure of β event-related desynchronization (ERD) during syllable sequence execution. MethodsEEG data was collected from 128 channels in a 5 minute, eyes-open resting state condition and from a syllable sequence repetition task. Temporal independent component analysis (ICA) was used to separate volume conducted EEG sources and to find a set of component weights common to the RS and syllable repetition task. Both traditional measures of power spectral density (PSD) and parameterized spectra were computed for components showing peaks in the β band (13-30Hz). Parameterization was used to evaluate separable components adjusted for the 1/f part of the spectrum. ResultsICA revealed frontal-parietal midline and lateral sensorimotor (μ) components common to the RS and a syllable repetition task with peaks in the β band. The entire spectrum for each component was modeled using the FOOOF algorithm. Independent samples t-tests revealed significantly lower periodic β in midline central-parietal and lateral sensorimotor components in AWS. Regression analysis suggested a significant relationship between left periodic sensorimotor β power in the RS and ERD during syllable sequence execution. ConclusionsFindings suggest that periodic β peaks in the spectrum are related to hypothesized underlying pathophysiological differences in stuttering, including midline rhythms associated the default mode network (DMN) and lateral sensorimotor rhythms associated with the control of movement.

AI-Based Risk Assessments in Forensic Auditing: Benefits, Challenges and Future Implications

Forensic auditing is a critical component of ensuring financial integrity and detecting fraud within organizations. Traditional methods of risk assessment in forensic auditing often rely on manual processes, which can be time-consuming, labour-intensive, and prone to human error. In recent years, the integration of artificial intelligence (AI) techniques has revolutionized the field, offering more efficient and accurate risk assessment capabilities. This abstract explores the role of AI-based risk assessment in forensic auditing, highlighting its benefits, challenges, and future implications. AI-based risk assessment leverages advanced algorithms and machine learning models to analyse large volumes of financial data, identify patterns, anomalies, and potential red flags indicative of fraudulent activities. By automating repetitive tasks such as data collection, classification, and analysis, AI streamlines the auditing process, enabling forensic auditors to focus on interpreting results and making informed decisions. One of the primary advantages of AI-based risk assessment is its ability to detect complex fraud schemes that may go unnoticed by traditional methods. Machine learning algorithms can detect subtle deviations from expected behaviour, flagging transactions or activities that exhibit unusual patterns or characteristics. Moreover, AI systems can adapt and learn from new data, continuously improving their detection capabilities over time. Another benefit of AI-based risk assessment is its scalability and efficiency. With the increasing volume and complexity of financial transactions, manual auditing processes struggle to keep pace. AI, on the other hand, can analyse vast datasets in a fraction of the time it would take a human auditor, allowing organizations to conduct more comprehensive and timely audits.

HUMAN RESOURCE MANAGEMENT AND ARTIFICIAL INTELLIGENCE: TRANSFORMATIVE EFFECTS AND FUTURE PROSPECTS

Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. Ancient civilizations contained tales of intelligent robots, such as Greek and Chinese cultures. AI as a modern phenomenon which took shape in the mid-20th century with the advent of digital computers. The first breakthrough, published in 1950, was Alan Turing's paper, ‘Computing Machinery and Intelligence. AI can make provide detailed solution to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car). Many sectors like finance, healthcare, to human resource management (HRM), rely on AI technologies. AI's integration in HRM has brought about great change in conventional HR practices used to measure efficiency, accuracy, strategic decision-making, etc. AI technologies help to provide solution to automation of repetitive tasks, analysis of sprawling volumes of data and insights facilitating decision-making. Through this research paper we try to understand the AI's effects on HR processes, in terms of recruitment, onboarding, performance appraisal, training and development, employee engagement, workforce planning; understanding the benefits and cons of implementing AI in HRM; ethical considerations and potential biases in AI algorithms relevant to HR practices; analysis of emergent AI trends with respect to HRM. Trends in AI in HR tend towards a most promising future, presenting several innovations. Accordingly, HR professionals will be expected to acquire competency with regards to new roles and responsibilities to avail AI in enhancing strategic planning, ethical oversight, and data-driven decision-making.

THE AI-GENDER INTERSECTION: PIONEERING SOLUTIONS FOR WORK-LIFE BALANCE BEYOND TRADITIONAL HOURS

The paper explores the intersection of artificial intelligence (AI) and gender equality, focusing on how AI can address gender disparities in the workplace and enhance work-life balance. It begins by outlining the historical context of the traditional 9-5 work model and its implications for gender inequality, particularly the challenges faced by women in achieving career advancement and work-life balance. The research objectives include investigating the potential of AI technologies to reduce gender disparities, improve work-life balance, and foster unbiased hiring practices. Key findings highlight that AI can play a pivotal role in transforming workplace dynamics by automating repetitive tasks, enabling flexible work arrangements, and providing personalized support for employees. Case studies illustrate successful implementations of AI in recruitment processes, showing how organizations like Unilever and Hilton have utilized AI to promote fairer hiring practices and enhance diversity. However, the paper also addresses the risks of AI reinforcing existing biases if not carefully managed. The significance of this research lies in its implications for policymakers, employers, and workers. By leveraging AI responsibly, organizations can create more equitable workplaces that accommodate diverse needs and promote gender parity. The paper concludes with recommendations for ethical AI use, emphasizing the importance of transparency, accountability, and continuous monitoring to mitigate biases and ensure that AI technologies benefit all employees.

Performance assessment of a multi-level shuttle system: an optimisation-embedded simulation study on time and energy consumption

ABSTRACT In response to the dynamic demands of modern e-commerce and supply chains, this paper addresses the critical need for adaptability and efficiency through the implementation of automated storage and retrieval systems. By using an optimisation-embedded simulation methodology, the proposed study focuses on a multi-level shuttle system designed to overcome the inherent limitations of traditional storage systems. A comprehensive discrete event simulation model is developed to evaluate the performance of the multi-level shuttle system. This model includes calculations of travel time and energy consumption, taking also into account stochastic handling times associated with repetitive tasks. In addition, a dynamic programming algorithm is integrated in the simulation model to optimise the sequencing of storage and retrieval missions performed by the handling machine. The study systematically investigates various design and operational parameters, including warehouse configurations and capacity, simultaneous handling of multiple unit loads, and the dimensions of the optimisation sessions. For all configurations tested, the research demonstrates operational advantages in the simultaneous handling of two unit loads. Theoretical contributions extend the current state of simulation models for automated warehousing systems. For practitioners, the study provides empirical insights to support decision making in the configuration of automated warehouses.

The use of accelerometers to assess upper limb function in patients with obstetric brachial plexus palsy

For half a century, the Mallet Scale (MS) has been utilized to assess upper limb function in patients with obstetric brachial plexus palsy (OBPP). However, the correct use of the MS requires trained personnel and the MS does not measure compensatory movements. For this reason, new methods are needed to compensate for these weaknesses. This study introduces an innovative method for objective functional motion analysis using accelerometers to measure upper limb movements in thirty patients with obstetric brachial plexus lesions. Five triaxial accelerometers were positioned on the chest and each upper limb. They recorded acceleration signals during repetitive everyday tasks: hand-to-mouth (HM), hand-to-neck (HN), and hand-to-spine (HS). From these signals, 54 features were extracted and subjected to linear correlation tests to identify 5 suitable features. An algorithm was then developed to categorize patients into five groups and compute an individual movement performance score (iMPScore) assessing the patient’s upper extremity function. By using the iMPScore more than 75% of all participants have been classified correctly with respect to their MS category. Identification of MS I category patients in general and assessing upper extremity function of MS I to III in HS tasks were most challenging. We conclude that the introduced approach is a valuable tool for gauging movement limitation of upper limbs in patients with obstetric brachial plexus palsy. Compared to other clinically established methods, it becomes possible to record and even quantify the extent of compensatory movements. In this way, an objective, user- and patient-friendly method is offered, which supports significantly physicians and therapists in their evaluation of OBPP.

Caribbean libraries 2.0: the robotic revolution in Caribbean academic library spaces

Purpose This paper is the genesis for robots and robotic technology and their introduction to the Caribbean Academic library community. This paper aims to explore the specific areas that this technology can improve as well as their adaptability and dynamic yet multifaceted nature it possesses. Design/methodology/approach A thorough assessment of literature was done of all developed libraries that are employing the services of robots and robotic technology in their daily operations. Additionally, a meticulous analysis was done of all Caribbean Libraries that have explored, are currently exploring or actively explored the implementation of robots and robotic technology for effective use in their libraries. Findings Seamless functionality as well as the reduction of mundane repetitive tasks by library staff is at the fore. Efficacy and heightened levels of accuracy are also found to be a great factor for implementation as well as speed of retrieval and offsite storage are further benefits to the implementation of robots and robotic technology. Research limitations/implications This research primarily assessed material on robotics and robotic technology that offers unprecedented efficacy and accuracy in the processing of information and tasks assigned as well as smooth location and retrieval of library material resulting in reduction in wait time for all library users. Originality/value To the best of the author’s knowledge, this paper is the first of its kind and is intended to trigger a “light bulb” in the minds of decision-makers and managers of Library spaces as to the potential robots and robotic technology has on fostering greater levels of efficacy in certain key areas of libraries and help improve user services while adding to the theoretical body of knowledge available in the field on this fast rising area.

Fatigue assessment for back-support exoskeletons during repetitive lifting tasks.

Fatigue is a major cause of low back pain for workers in various fields, including industry and agriculture. It has a negative impact on workers' safety, decreases their productivity, and causes a reduction in their occupational career. An exoskeleton is expected to be a solution for reducing workers' fatigue. However, assessing the safety and effectiveness of exoskeletons, except for the direct measurement of electromyography (EMG) in the human body, is challenging in real-case scenarios. Recently, simulations have been widely used to estimate biomechanical variables. Thus, we aimed to develop a method that combines an exoskeleton model and human body simulation to evaluate the effects of exoskeletons on lumbar fatigue. The strength and tendency estimated using this method are similar to those obtained from EMG devices in symmetrical repetitive lifting tasks. In addition, this method can be used to predict and simulate fatigue after a recorded motion. Our findings will help guide manufacturers in designing their products.

Application of Rain Classroom in College English Teaching: An Exploratory Analysis of Teacher Experience on Workload and Efficiency

This study aims to explore the impact of the Rain Classroom platform on teachers’ workload and efficiency in college English classrooms. Through semi-structured interviews and classroom observations with several university English teachers who have used Rain Classroom, the findings indicate that while the platform reduces certain repetitive tasks and enhances classroom interaction efficiency, it also increases the technological learning and operational burden on teachers. The study provides valuable theoretical and practical insights for the effective application of educational technology in language teaching.

The Rise of Bot-Driven Fraud: Understanding Threats and Implementing Advanced Prevention Strategies

In today’s digital landscape, the proliferation of bots has fundamentally changed the cybersecurity ecosystem. While bots were initially designed to automate simple, repetitive tasks, they have since evolved into sophisticated tools used by cybercriminals to carry out large-scale attacks. From brute force attacks and credential stuffing to account takeovers and denial of service (DoS) attacks, bots pose a growing threat to businesses across all sectors. The damage caused by bot-driven fraud extends far beyond financial losses—it affects operational efficiency, customer trust, and brand reputation. This paper explores the various types of bot-driven fraud, the consequences businesses face as a result of these activities, and the advanced prevention strategies that are being developed to counter these threats. By understanding how bots are used in cybercrime and implementing proactive defense mechanisms, businesses can reduce their vulnerability to fraud and better protect their customers and assets.

Which Factors Predict L2 Receptive Vocabulary and Expressive Syntax in Bilingual Children from Low-SES Families?

The objective of the current study was two-fold. First, it aimed to estimate receptive vocabulary and expressive syntax skills in L2 Italian among early sequential/simultaneous bilingual children of migrant single-mother families with very low socioeconomic status (SES). This objective was achieved by matching the participants' performance with normative data. Secondly, this study aimed to identify which individual and language exposure factors contributed to learning L2 vocabulary and syntax. Twenty-four early sequential/simultaneous bilingual children (age range = 5.10-12.4 years) and their mothers were enrolled. Mothers answered questions about linguistic biography and demographic information. Children completed Lexical Comprehension, Sentence Repetition, and Non-Word Repetition tasks from the Language Assessment Battery for 4-12-year-olds to, respectively, assess receptive vocabulary, expressive syntax, and phonological processing. Moreover, non-verbal intellectual functioning was evaluated by the Raven's Test. Compared to normative data, 20 children showed lower receptive vocabulary abilities (<-1.5 SD), 24 lower expressive syntax skills (-2DS), and 7 children lower phonological processing (<-1.5 DS). Moreover, L2 phonological processing and the length of L2 exposure in an educational context positively predicted L2 receptive vocabulary as well as L2 expressive syntax skills. To date, performance in L2 among early sequential/simultaneous bilingual children from migrant households and very low SES remains underexplored. Future efforts need to be directed towards the understanding of factors that impact oral competence in L2, considering that these children will also be exposed to written L2 in the school context.

Prevalence of Musculoskeletal Disorders among Dental Professionals: An Umbrella Review

AbstractPerforming repetitive tasks on patients' mouths increases the risk of strain-related injuries for dental professionals, ultimately leading to musculoskeletal disorders (MSDs). This umbrella review aims to assess the scope of the MSD problem and identify the most often impacted body parts among dental professionals. A comprehensive search of the literature was performed from inception to March 30, 2024, in PubMed-MEDLINE, Embase, Scopus, and Web of Science databases. This review evaluated systematic reviews (SRs) and meta-analyses (MAs) of the observational studies that reported the prevalence of MSDs or any form of MSD among dental professionals such as dentists, dental hygienists, or dentistry students. The extracted data were complemented with narrative synthesis. A total of eight SRs and MAs were included in this umbrella review. Five of the eight studies were conducted globally, one in the context of Western countries and two in an Iranian context. Four of the eight studies focused on MSDs in dental practitioners, with prevalence rates ranging from 0.5 to 93%. Four body areas, that is, the neck, the lower and upper back, the shoulders and wrists, and hands were widely exposed to MSDs, with significant prevalence reported across studies. In conclusion, MSDs are common among dental professionals. Consequently, there is a pressing need to implement infrastructural and behavioral ergonomic solutions in the dental profession. However, a major concern arises from the critically low quality of available studies on MSD prevalence in dental professionals. Therefore, more rigorous research adhering to the basic quality criteria is required.